Abstract: Given its advantages of safety, convenience, punctuality and comfort, the subway has become a primary mode of transportation in people’s daily lives. As urban development continues, the pressure on subway passenger flow increases daily. Oversaturation of passenger flow, caused by various factors, leads to safety hazards such as station congestion, reduced operational efficiency, and even crowd crush incidents. These issues significantly affect passenger safety and the operational management of transportation departments. Therefore, accurate passenger flow prediction is crucial for supporting reasonable travel arrangements and informed decision-making by subway operation and management departments.
Passenger flow data exhibits both temporal dependence and strong spatial correlations, along with clear periodic patterns. However, existing prediction models struggle to balance long-term and short-term dependencies when addressing spatial-temporal correlations and periodicity effectively. Consequently, this paper develops a combinatorial prediction model for subway passenger flow, integrating dual-dimensional temporal and spatial information while considering periodic characteristics. Theoretically, this study aims to accurately predict passenger flows at subway stations, enhancing the accuracy of current models. Practically, the findings can support governmental decisions regarding subway construction, assist transportation management in resources allocation, minimize resource wastage, and offer the public travel planning references.
Initially, existing passenger flow forecasting technologies are reviewed on the Internet, their advantages and disadvantages are analyzed, and insights are gained to inform the design of our research plan. Subsequently, the card swiping records of Hangzhou Metro and the adjacency matrix of Hangzhou Metro transportation network, provided by the Hangzhou Municipal Public Security Bureau, are selected as the dataset for this study. Various factors affecting passenger flow are identified. We choose to consider and collect relevant data from three aspects: travel mode, weather conditions, and historical data at corresponding time points, to construct relevant features. Finally, to mitigate any negative impact of the constructed features on the model’s prediction results, LightGBM importance analysis and Pearson correlation analysis are employed to select features, thereby enhancing the model’s generalization ability.
In this paper, we propose a combined model, GCN-DFInformer, for subway passenger flow prediction, which integrates a graph convolutional network (GCN) and an improved Informer model (DCC-FECAM-Informer, DFInformer) in parallel. Firstly, an inflated causal convolutional self-attention mechanism is introduced to compensate for the Informer’s insensitivity to local information and to enhance the model’s ability to capture trend changes and local fluctuations in passenger flow over short periods. Secondly, considering the obvious periodicity and seasonality of subway passenger flow data, the frequency enhanced channel attention mechanism (FECAM) is introduced to improve the model’s ability to identify and utilize inherent features in the data series for better predictions. Finally, the prediction is performed by the parallel-connected graph convolutional network to achieve the fusion of temporal and spatial information. The experiments comparing the model’s predictions with actual values demonstrate that the GCN-DFInformer model exhibits strong predictive performance and robustness. The experimental results from the scenario dataset test demonstrate that, compared to other models, the proposed model yields smaller errors and a higher coefficient of determination (R²). This validates the superior predictive performance of the model and highlights its effectiveness in improving the accuracy of subway passenger flow prediction.
The proposed model significantly enhances prediction accuracy by integrating the Informer, dilated causal convolutional self-attention mechanism, FECAM module, and graph convolutional network. This model leverages the strengths of both GCN and DFInformer, and their parallel structure helps maintain the independence of spatial and temporal information. In the next phase, additional influencing factors related to the station will be incorporated, such as the functional area where the station is located, and other auxiliary information like the subway operation timetable, to further improve the model’s prediction accuracy.

Key words: subway passenger flow forecast, Informer, graph convolutional networks, dilated causal convolution, attention mechanism, frequency enhanced channel

摘要： 地铁客流量预测对于交通管理和安全具有重要意义。针对当前预测模型在兼顾客流量数据时空相关性和周期性特征时,长短期依赖处理不均衡而影响客流预测精度的问题,提出一种融合GCN和改进Informer的预测模型。首先,采用膨胀因果卷积自注意力机制,增强模型对客流量短时间内趋势变化和局部波动的捕捉能力;其次,引入频率增强信道注意力机制,提高模型对地铁客流量中固有周期性特征的识别与利用能力;最后,通过并行的图卷积网络进行时间和空间两个维度的信息融合实现预测。场景数据集测试实验结果表明,与其他模型对比,其各项误差更小、决定系数更高,验证其预测效果更佳,能够有效地提升地铁客流预测的准确性。

关键词: 地铁客流量预测, Informer模型, 图卷积网络, 膨胀因果卷积, 注意力机制, 频率增强信道